A surge of recent research has shown that machine learning applied to satellite imagery can predict socio-economic characteristics at a fine spatial scale with reasonable accuracy [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. In data-scarce parts of the world, these prediction methods have the potential to dramatically increase policymakers' understanding of the spatial dimensions of economic well-being, and support decisions about targeting poverty assistance, program implementation choices, and evaluation. Satellite imagery-based predictions over a wide variety of tasks have been shown to perform reasonably well – and the state of the science is quickly improving [8]}.
